How to harness the power of data science to give customers their rightful place on the board’s agenda
When I founded Underlined in 2011, my main goal was to give the customer a much more prominent place in business decisions. In many organisations I saw that attempts were made to give customers their rightful place, but all too often these failed.
In recent years, Customer eXperience (CX) has been on the move. CX wants to make a difference, but how do you prevent making the same mistakes? Management still focusses primarily on company growth and returns, but the CX professional fully understands that an optimal customer experience is needed to achieve this. Sounds logical, but therein lies the biggest challenge: how to solve the dilemna of management focussing on company value and CX people focussing on customer value?
The real answer is: link customer value to company value!
The old CRM adage, where it is all about the value the customer can offer to the company, no longer applies in the CX era. CX has turned it around totally: ask not what a customer can do for you, but ask what you can do for a customer!
The most commonly used CX measurement to make this customer value transparent to the management is the NPS (Net Promoter Score). The NPS is a powerful tool, but it has its own achilles heel. It has started to lead a life of its own: over the past few years, the actual number of feedback measurements have exploded and the underlying questionnaires have grown steadily, all because we seek to know more and more. It is used as direct input to feed agile backlogs. This in turn results in more questions and more continuous feedback. The big question behind the question is: are we not losing sight of our actual goal? Are we operating in ever decreasing circles and getting inferior feedback from an increasingly small group of customers?
As an external benchmark, national authorities (such as the AFM and DNB for financial services in the Netherlands) are also using the NPS as a yardstick for comparing and judging competing companies in terms of CX performance. This ensures ample attention from management for the NPS but the question is, is this attention based on the correct intention?
The use of data science in the CX market is most likely to give the CX professional access to better tools to engage management in the correct way. The challenge here is to understand how you can really link customer and company value so that any subsequent action you take will actually contibute rather than deter from this.
In recent years, Underlined, in partnerring with clients and universities, has gained a lot of experience in this challenging and exciting field of work. We see 5 distinct phases that organisations traverse when they seek to use NPS as originally intended i.e. the NPS measurement should place the customer firmly in the boardroom, so that the best data driven decisions can be made. Doing so is always highly beneficial to both customer and organisation alike.
Figure: The 5 phases working towards structural insights in value for customer and value for the company.
Stage 1: Let’s Start Data Driven CX!
Building knowledge and inspiration for your approach! What is at the heart of your data driven approach? Is it about making customer behaviour and feedback transparent and how this can be deployed to address improvements? Or would you also like to influence omnichannel customer behaviour? These and other questions are going to be explored at this stage and you will learn how to start applying this.
Phase 2: NPS Open Feedback
In this phase, you will start using answers to open questions in customer feedback for your insights. With text analysis, this can be translated into useful data for insights and guidance. Open feedback can be found in completed NPS surveys, but also in web reviews, social media posts and emails. Using all of this feedback and linking this to opportunities for improvement is usually a first step in improving customer value.
Phase 3: CX Insights Framework
When you work with your first insights from all kinds of feedback sources, you start to see data driven opportunities. A question that soon arises is: can I really link customer value to company value so that when management starts to address an issue it can be directly translated into extra value for the customer? Central to this phase is the link between customer KPIs and actual customer behavior and feedback. This is a blueprint for determining what impacts the KPIs and what improves the customer relationship. It also translates what happens in a particular silo and its consequences for the customer experience across all silos i.e. the entire customer journey.
Phase 4: Data Science Models
With the CX framework in place, you are ready to get more advanced: applying Data Science Magic to gain insight into what impacts your CX KPIs and how customer behavior develops over time. The most widely used models in journey mining are, for example; process mining, driver statistics, impact models and predictive modelling. This gives additional insight into how you can link business value and customer value, using an fully integrated approach.
Phase 5: Mining of Emotional Journeys
For the cracks in data science, the superlative step is to make the emotional customer journey transparent and to predict future behaviour based on earlier behaviour and underlying emotions. In this phase, you will reduce NPS measurements and substitute this with better insights gained from other data sources. Data which looks at what customers actually do rather than what they say when prompted.
On 6 & 7 February, Underlined looked beyond the NPS. We believe that – to really bring the customer back to the board table – there are better alternatives to the current NPS. We want to share our knowledge and we hope to meet you during the MIE to discuss various customer examples, and how we can make the Netherlands more customer-oriented and how data science can contribute.
Theo van der Steen